Added city query examples, and workbench query screenshots

guides/structured-processing/load-doc.md

@@ -44,7 +44,7 @@ For this example, we'll use a sample document containing city demographic data t
 
 1. **Access the Sample Document**
    Open this Google Docs document containing city demographic data:
-   [https://docs.google.com/document/d/1_5KfhWL9fN3VuhANIVKuJX6MVBH3vFVW3RiUNYE9jHQ/edit](https://docs.google.com/document/d/1_5KfhWL9fN3VuhANIVKuJX6MVBH3vFVW3RiUNYE9jHQ)
+   [https://raw.githubusercontent.com/trustgraph-ai/example-data/main/cities/most-populous-cities.pdf](https://raw.githubusercontent.com/trustgraph-ai/example-data/main/cities/most-populous-cities.pdf)
 
 2. **Save as PDF**
    - In Google Docs, click **File** → **Download** → **PDF Document (.pdf)**
@@ -121,6 +121,12 @@ e.g. `Object extraction`.
 
 ## Step 3: Launch Document Processing
 
+When loading the document on the workbench, it can help to decide to store
+the data in a particular collection for later.  Click the dialog
+top right, and set the collection to `cities`.
+
+<img src="set-collection.png" alt="Set collection option"/>
+
 On the Library page, select your document containing city information,
 click 'Submit' at the bottom of the screen.
 

guides/structured-processing/load-file.md

@@ -49,9 +49,9 @@ Before starting this guide, ensure you have:
 
 ## Data files you will need:
 
-- [UK pies](https://drive.google.com/file/d/1u0DzP5bu15sSwnHldpZTVXUNoVo5DzFQ/view?usp=sharing)
-- [French pies](https://drive.google.com/file/d/1xHBYLkrbB1NmJeeXNRlQUuQCQyPThuN-/view?usp=drive_link)
-- [Pies Structured Descriptor Language](https://drive.google.com/file/d/1ALuMuwRy8m_hUk2Y_ftFLHK44TwhNUv3/view?usp=drive_link)
+- [UK pies](https://raw.githubusercontent.com/trustgraph-ai/example-data/main/pies/uk-pies-simplified.xml)
+- [French pies](https://raw.githubusercontent.com/trustgraph-ai/example-data/main/pies/fr-pies-simplified.xml)
+- [Pies Structured Descriptor Language](https://raw.githubusercontent.com/trustgraph-ai/example-data/main/pies/pies-sdl.json)
 
 ## Step 1: Define a Schema
 
@@ -379,6 +379,10 @@ and collection load.
 
 ## Notes
 
+- At the time of writing, the prompts work well at XML processing, 
+  but we'll be optimising to work with smaller models and provide
+  better coverage of other data types.  We recommend you stick with
+  XML data for TrustGraph 1.3.
 - You may find that the prompts are sensitive to different LLMs, and
   that you may see hallucinations or insensitivity to different data features.
 - XPath expressions have some incompatibilities and edge cases with

guides/structured-processing/query.md

@@ -32,11 +32,46 @@ Before starting this guide, ensure you have:
 - Python 3.10 or later with the TrustGraph CLI tools installed (`pip install trustgraph-cli`)
 - Sample documents or structured data files to process
 
+## Workbench
+
+The Structured Query page on the workbench UI allows you to run the
+queries we'll be running here.  Make sure:
+
+- You have set the collection parameter correctly in the session state
+  popover, top-right.
+- Be sure to set a flow which has object processing enabled e.g. the
+  `obj-ex` flow which you created if you are following this guide.
+
+<img src="nlp-query.png" alt="NLP query"/>
+
+<img src="structured-query.png" alt="Structured query"/>
+
 ## NLP query operation
 
 This operation takes a natural language query, and uses an LLM prompt
 to convert to a GraphQL query.  This uses defined schema, so you need
-to have the pies schema loaded:
+to have the schemas loaded in the previous guide steps.
+
+This is a building block for more complete functionality, but it may
+be useful for you to be able to look at converted queries to check that
+your application is performing well.
+
+```bash
+tg-invoke-nlp-query -f obj-ex -q 'Cities with more than 22.8m people'
+```
+
+If successful the output is something like...
+
+```
+Generated GraphQL Query:
+----------------------------------------
+query { cities(where: {population: {gt: 22800000}}) { city country population } }
+----------------------------------------
+Detected Schemas: cities
+Confidence: 95.00%
+```
+
+Querying the pies data:
 
 ```
 tg-invoke-nlp-query -f obj-ex \
@@ -59,6 +94,31 @@ Confidence: 95.00%
 This operation takes a GraphQL query, and executes it on the object
 store.
 
+City example:
+
+```
+tg-invoke-objects-query -f obj-ex --collection cities -q '
+{
+  cities(where: {population: {gt: 22800000}}) { city country population }
+}
+'
+```
+
+```
++-----------+------------+------------+
+|   city    |  country   | population |
++-----------+------------+------------+
+| Shanghai  |   China    |  30482140  |
+| São Paulo |   Brazil   |  22990007  |
+|   Delhi   |   India    |  34665569  |
+|   Tokyo   |   Japan    |  37036204  |
+|   Dhaka   | Bangladesh |  24652864  |
+|   Cairo   |   Egypt    |  23074225  |
++-----------+------------+------------+
+```
+
+Pies example:
+
 ```
 tg-invoke-objects-query -f obj-ex \
   --collection uk-pies \
@@ -86,6 +146,28 @@ You can use `--format` to request CSV or JSON output.
 
 This is an API which uses the above two operations in sequence.
 
+Cities example:
+
+```
+tg-invoke-structure-query -f obj-ex --collection cities \
+  -q 'Cities with more than 22.8m people'
+```
+
+```
++-----------+------------+------------+
+|   city    |  country   | population |
++-----------+------------+------------+
+| Shanghai  |   China    |  30482140  |
+| São Paulo |   Brazil   |  22990007  |
+|   Delhi   |   India    |  34665569  |
+|   Tokyo   |   Japan    |  37036204  |
+|   Dhaka   | Bangladesh |  24652864  |
+|   Cairo   |   Egypt    |  23074225  |
++-----------+------------+------------+
+```
+
+Pies example:
+
 ```
 tg-invoke-structured-query -f obj-ex \
   --collection uk-pies \
@@ -107,6 +189,9 @@ You can use `--format` to request CSV or JSON output.
 
 ## With collections
 
+Using the same schema with different collections allows you to group
+data:
+
 ```
 tg-invoke-structured-query -f obj-ex \
   --collection fr-pies \